Development of Specialized Deep-Learning Models for Crop Freshness Assessment to Mitigate Post-Harvest Loss

Traditional methods for evaluating crop ripeness are critiqued for their inefficiency and potential harm to produce. The use of image-processing and deep-learning techniques can solve these issues as a trend in non-destructive methods. However, an overfitting problem arises when optimization and generalization are used to estimate the parameters of the next epoch. In this paper, we develop specialized models with a high volume of training images for a single type of crop to achieve the goal of 100% accuracy for both test and validation datasets. This development contributes insights into leveraging deep learning for crop assessment, emphasizing its potential application in diverse agricultural scenarios. Experimental results show that the proposed models are superior to several existing available methods.